



'''simple docstring'''

import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  logging.get_logger(__name__)

class 							a__:






			def __init__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Optional[int]:
					snake_case__	     =question_encoder
					snake_case__	     =generator
					snake_case__	     =self.question_encoder






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Tuple:
					if os.path.isfile(_UpperCAmelCase						):
							raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""						)
					os.makedirs(_UpperCAmelCase					,   exist_ok=_UpperCAmelCase						)
					snake_case__	     =os.path.join(_UpperCAmelCase					,   'question_encoder_tokenizer'						)
					snake_case__	     =os.path.join(_UpperCAmelCase					,   'generator_tokenizer'						)
					self.question_encoder.save_pretrained(_UpperCAmelCase						)
					self.generator.save_pretrained(_UpperCAmelCase						)






			@classmethod
			def 						_lowercase  (     cls					,   _UpperCAmelCase					,   **_UpperCAmelCase						)							->							Dict:
					# dynamically import AutoTokenizer
					from ..auto.tokenization_auto import AutoTokenizer

					snake_case__	     =kwargs.pop('config'					,   _UpperCAmelCase						)

					if config is None:
							snake_case__	     =RagConfig.from_pretrained(_UpperCAmelCase						)

					snake_case__	     =AutoTokenizer.from_pretrained(
					    _UpperCAmelCase					,   config=config.question_encoder					,   subfolder='question_encoder_tokenizer'						)
					snake_case__	     =AutoTokenizer.from_pretrained(
					    _UpperCAmelCase					,   config=config.generator					,   subfolder='generator_tokenizer'						)
					return cls(question_encoder=_UpperCAmelCase					,   generator=_UpperCAmelCase						)






			def __call__(     self					,   *_UpperCAmelCase					,   **_UpperCAmelCase						)							->							str:
					return self.current_tokenizer(*_UpperCAmelCase					,   **_UpperCAmelCase						)






			def 						_lowercase  (     self					,   *_UpperCAmelCase					,   **_UpperCAmelCase						)							->							Dict:
					return self.generator.batch_decode(*_UpperCAmelCase					,   **_UpperCAmelCase						)






			def 						_lowercase  (     self					,   *_UpperCAmelCase					,   **_UpperCAmelCase						)							->							Any:
					return self.generator.decode(*_UpperCAmelCase					,   **_UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__	     =self.question_encoder






			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     =self.generator






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None					,   _UpperCAmelCase = None					,   _UpperCAmelCase = None					,   _UpperCAmelCase = "longest"					,   _UpperCAmelCase = None					,   _UpperCAmelCase = True					,   **_UpperCAmelCase					,   )							->							BatchEncoding:
					warnings.warn(
					    '`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
					    'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
					    'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
					    'details'					,   _UpperCAmelCase					,   )
					if max_length is None:
							snake_case__	     =self.current_tokenizer.model_max_length
					snake_case__	     =self(
					    _UpperCAmelCase					,   add_special_tokens=_UpperCAmelCase					,   return_tensors=_UpperCAmelCase					,   max_length=_UpperCAmelCase					,   padding=_UpperCAmelCase					,   truncation=_UpperCAmelCase					,   **_UpperCAmelCase					,   )
					if tgt_texts is None:
							return model_inputs
					# Process tgt_texts
					if max_target_length is None:
							snake_case__	     =self.current_tokenizer.model_max_length
					snake_case__	     =self(
					    text_target=_UpperCAmelCase					,   add_special_tokens=_UpperCAmelCase					,   return_tensors=_UpperCAmelCase					,   padding=_UpperCAmelCase					,   max_length=_UpperCAmelCase					,   truncation=_UpperCAmelCase					,   **_UpperCAmelCase					,   )
					snake_case__	     =labels['input_ids']
					return model_inputs









'''simple docstring'''

import os

SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  {'''I''': 1, '''V''': 5, '''X''': 1_0, '''L''': 5_0, '''C''': 1_0_0, '''D''': 5_0_0, '''M''': 1_0_0_0}






def 				a  (     UpperCamelCase_    :    str			)       ->	int:
		snake_case__	     =0

		snake_case__	     =0
		while index < len(UpperCamelCase_			) - 1:
				snake_case__	     =SYMBOLS[numerals[index]]
				snake_case__	     =SYMBOLS[numerals[index + 1]]
				if current_value < next_value:
						total_value -= current_value
				else:
						total_value += current_value
				index += 1
		total_value += SYMBOLS[numerals[index]]

		return total_value






def 				a  (     UpperCamelCase_    :    int			)       ->	str:
		snake_case__	     =''

		snake_case__	     =num // 1000
		numerals += m_count * "M"
		num %= 1000

		snake_case__	     =num // 100
		if c_count == 9:
				numerals += "CM"
				c_count -= 9
		elif c_count == 4:
				numerals += "CD"
				c_count -= 4
		if c_count >= 5:
				numerals += "D"
				c_count -= 5
		numerals += c_count * "C"
		num %= 100

		snake_case__	     =num // 10
		if x_count == 9:
				numerals += "XC"
				x_count -= 9
		elif x_count == 4:
				numerals += "XL"
				x_count -= 4
		if x_count >= 5:
				numerals += "L"
				x_count -= 5
		numerals += x_count * "X"
		num %= 10

		if num == 9:
				numerals += "IX"
				num -= 9
		elif num == 4:
				numerals += "IV"
				num -= 4
		if num >= 5:
				numerals += "V"
				num -= 5
		numerals += num * "I"

		return numerals






def 				a  (     UpperCamelCase_    :    str = "/p089_roman.txt"			)       ->	int:
		snake_case__	     =0

		with open(os.path.dirname(UpperCamelCase_			) + roman_numerals_filename			) as filea:
				snake_case__	     =filea.readlines()

		for line in lines:
				snake_case__	     =line.strip()
				snake_case__	     =parse_roman_numerals(UpperCamelCase_			)
				snake_case__	     =generate_roman_numerals(UpperCamelCase_			)
				savings += len(UpperCamelCase_			) - len(UpperCamelCase_			)

		return savings


if __name__ == "__main__":
			print(f"""{solution() = }""")




